Chance News 81

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Eminence based medicine—The more senior the colleague, the less importance he or she placed on the need for anything as mundane as evidence. Experience, it seems, is worth any amount of evidence. These colleagues have a touching faith in clinical experience, which has been defined as “making the same mistakes with increasing confidence over an impressive number of years.” The eminent physician's white hair and balding pate are called the “halo” effect.

from Seven alternatives to evidence based medicine, British Medical Journal, 18 December 1999

Submitted by Paul Alper

"Alternative therapists don't kill many people, but they do make a great teaching tool for the basics of evidence-based medicine, because their efforts to distort science are so extreme."

Ben Goldacre, in What eight years of writing the Bad Science column have taught me, Guardian, 4 November 2011

Submitted by Bill Peterson

"Of course, from the quasi-experimental perspective, just as from that of physical science methodology, it is obvious that moving out into the real world increases the number of plausible rival hypotheses. Experiments moe to quasi-experiemtns and on into queasy experiments, all too easily."

Donald T. Campbell, in Methodology and Epistemology for Social Science: Selected Papers, page 322.

Submitted by Steve Simon


Question of significance

“Ultrasounds Detect Cancers That Mammograms Missed, Study Finds”
by William Weir, The Hartford Courant, January 13, 2012

A 2009 CT law requires that “all mammogram reports include the patients' breast density information, and that women with greater than 50 percent density be recommended for additional ultrasound testing.” CT is apparently the first state to pass such a law.

For the period October 2009 to 2010, a University of Connecticut Hospital radiologist collected data on more than 70,000 cases, of which about 8,600 involved ultrasound screenings, and she found that the screenings “detected 3.25 cancers per 1,000 women that otherwise would have been overlooked.”

"When you think about it, we find four or five per thousand breast cancers in an overall screening population. So, then you add that extra three on," she said. "I think that's not insignificant."

Note that:

[The radiologist] told state officials that more data was needed to know whether ultrasound tests actually did a better job detecting tumors in breasts with high density. Ultrasounds typically cost patients more than a mammogram (particularly if their insurance has a high deductible), require skilled technologists and take longer to perform than a mammogram. .... [S]he called [the bill] a case of "putting the cart before the horse," [but that] the law presented a "golden opportunity."

The radiologist’s study has been accepted by publication in The Breast Journal.


  1. The radiologist commented that the finding of 3 additional cases of breast cancer per 1000 through the added ultrasound procedure - beyond the 4 or 5 per 1000 found through previous mammograms - was "not insignificant." Statistically speaking, what do you think she meant by that? Do you consider the phrase "not insignificant" equivalent to the term "significant," in a statistical context?
  2. Suppose that her finding was statistically significant. Do you think that it was, in a real-life sense, significant enough to justify the costs of an additional ultrasound screening, in time and/or money to a patient, to her insurer, or to a health facility?
  3. Do you think that CT was "putting the cart before the horse"?

Submitted by Margaret Cibes

The problems with meta-analyses

I had written a more mathematical blog entry in May, 2009 (referenced in CN 59), denoting the logical and mathematical/statistical problems with meta-analyses, but since that time many more meta-analyses have been published, and the public has discussed these results as if they were clinical fact. It is important to understand that the results of a meta-analysis should be presented only as a hypothetical clinical result, to be tested forwards in a properly designed clinical format, and not accepted as proven fact (such as the recent suggestion that women who ingest calcium supplements increase their risk of heart disease). In brief, a meta-analysis collects several studies of the same problem, none of which reaches clinical or statistical significance, in the hopes that the sum can be greater than its parts, and that combining non-significant studies can reach a significant result!

Some readily understandable problems with meta-analyses:

  1. You are never told which studies the author rejects as not being acceptable for his/her meta-analysis, so you cannot form your own opinion as to the validity of rejecting those particular studies.
  2. The problem of the Simpson Paradox, or the Yule-Simpson Effect: sometimes all the included studies point in one direction as being clinically significant, but the meta-analysis points in exactly the opposite direction. Numerous illustrations of the paradox have been discussed over the years in Chance News; this post from 2004 demonstrated different ways of calculating Derek Jeter's batting average, with differing results, using the same data in each case.
  3. There are two different statistical models or assumptions by which the analyzer combines the effects of the individual studies: the fixed effects model and the random effects model. Each model makes different assumptions about the underlying statistical distribution of observed data, so each calculation produces different results.
  4. There are two different methods for measuring the effect of the clinical intervention: standardized mean difference or correlation. Each method produces a different end result.
  5. If we look at #3 and #4, we see immediately that there are four possible combinations of analyses, leadeing to four different conclusions for the same set of studies. No one paper shows all four combinations and all four possible results.
  6. Finally, the choice of what constitutes a "significant' effect in any of the included studies is purely arbitrary. When this question was studied by clinical psychologists, no two analytical scientists reached the same conclusions of what was significant in all the included studies.

We therefore see that the result of any meta-analysis is largely dependent on the analyzer, and the reader never has enough data to redo the analysis, so the results have to be taken on faith, which is hardly a scientific result.

"There are three kinds of lies: Lies, Damn Lies, and Statistics" --Mark Twain

Submitted by Robin Motz

Larry Summers on statistics

What you (really) need to know
Larry Summers, New York Times, 22 January 2012.

Nick Horton sent this reference to the Isolated Statisiticians list, along with the following excerpt (Summers' sixth point) on the value of statistics:

Courses of study will place much more emphasis on the analysis of data. Gen. George Marshall famously told a Princeton commencement audience that it was impossible to think seriously about the future of postwar Europe without giving close attention to Thucydides on the Peloponnesian War. Of course, we’ll always learn from history. But the capacity for analysis beyond simple reflection has greatly increased (consider Gen. David Petraeus’s reliance on social science in preparing the army’s counterinsurgency manual).

As the “Moneyball” story aptly displays in the world of baseball, the marshalling of data to test presumptions and locate paths to success is transforming almost every aspect of human life. It is not possible to make judgments about one’s own medical care without some understanding of probability, and certainly the financial crisis speaks to the consequences of the failure to appreciate “black swan events” and their significance. In an earlier era, when many people were involved in surveying land, it made sense to require that almost every student entering a top college know something of trigonometry. Today, a basic grounding in probability statistics and decision analysis makes far more sense.